Structured Data for LLMs

What structured data is, applied to AI systems

Structured data is markup added to a web page that states facts about its content in a standardized, machine-readable format, rather than leaving a system to infer them from ordinary sentences. JSON-LD is the format Google recommends, a block of code embedded in a page that follows the Schema.org vocabulary to describe things like the type of page, the organization behind it, the product it discusses or the questions it answers, each as a labeled property rather than prose a reader has to parse. This has been part of standard technical SEO for years, aimed originally at helping search engines build rich results, but the same markup increasingly helps AI systems too, since a language model summarizing or citing a page benefits from the same unambiguous signal a search engine does: what entity is this page about, what type of content is it, what specific facts does it state. The vocabulary itself, Schema.org, is a shared standard maintained jointly by Google, Microsoft, Yahoo and Yandex, not something any single AI company controls or has redefined for its own purposes, which is part of why the same markup serves both traditional search and AI systems at once.

How it works on a page

A page implements structured data by embedding a JSON-LD script block in its HTML, typically in the page head, containing a nested object that follows Schema.org's defined types and properties: an Organization type carries a name and logo, a Product type carries a price and availability, a FAQPage type carries a list of question-and-answer pairs matching what actually appears in the visible content. The markup is not meant to add facts a page does not otherwise state visibly; the guidance from Google and the broader structured-data community is that markup should describe what a user can already see, not introduce hidden claims solely for machine consumption. A crawler or AI system that fetches the page can then parse this block directly, resolving what would otherwise require inferring meaning from surrounding prose, and can do so with less ambiguity: a number is explicitly tagged as a price rather than guessed from context, a name is explicitly tagged as an organization rather than confused with an unrelated proper noun elsewhere on the page. Validating that the markup parses correctly is a separate step from writing it, since a small syntax error, like a missing comma or mismatched type, can make an entire block fail silently, showing no visible symptom on the page while the structured data itself goes unread by anything checking for it. Testing tools built around the Schema.org and Google Rich Results specifications exist specifically to catch that class of quiet failure before it costs a citation opportunity.

Why it matters for AI visibility

An AI system extracting information from a page to answer a question benefits from the same clarity structured data was built to provide search engines: knowing precisely what entity a page describes, what type of content it is, and what specific facts it states, rather than reconstructing that from unstructured prose. This lowers the ambiguity a system has to resolve before it can cite a page confidently, and a page with valid, matching structured data gives a system less room to misattribute a fact or overlook an answer that is present but not clearly labeled. It does not guarantee a citation on its own; structured data is one input among several, alongside crawler access and content that answers a question directly, and none of those substitutes for the others. A page can have flawless markup and still go uncited if AI crawlers cannot reach it, which is why structured data is best treated as one layer in a technical checklist rather than the whole of it.

Good practices

• Use JSON-LD rather than older microdata or RDFa formats, since it is the format Google recommends and the one most AI documentation references.
• Make sure the markup describes facts that are also visible on the page, not hidden claims added only for machines.
• Validate the markup after every deploy, since a small syntax error can make an entire block fail without any visible symptom on the page.
• Prioritize types that match how a page is actually used: FAQPage for genuine question-and-answer content, Product for commerce pages, Organization for entity identity.
• Keep entity details, like organization name and description, consistent across every page that references them, rather than letting different pages state conflicting versions.
• Pair structured data with the crawler-access checks in Truffle's LLM SEO guide, since markup an AI crawler cannot reach never gets read.

Common mistakes

• Adding structured data that states facts not actually visible on the page, which violates the guidance behind the standard.
• Never validating the markup after a deploy, so a silent syntax error goes unnoticed for months.
• Using FAQPage markup on content that is not really a question-and-answer format, just to add the tag.
• Letting entity details drift out of sync across pages, such as different phone numbers or descriptions for the same organization.

Related terms

• llms.txt: a separate, unconfirmed proposal for summarizing a site for AI, distinct from the established Schema.org standard structured data relies on.
• robots.txt for AI Crawlers: controls whether an AI crawler can reach a page at all, a prerequisite that structured data alone does not solve.
• Passage Ranking: the retrieval step that can pull a specific answer from a page, a process structured data can make less ambiguous.
• GPTBot: one of the AI crawlers that needs page access before it can read any structured data present.

Frequently asked questions